A Comparative Study of Stochastic Model Predictive Controllers

Gonzalez, E. David; Sanchis, Javier; García-Nieto, Sergio; Salcedo, J. V.

doi:10.3390/electronics9122078

Cited by 16 publications

(14 citation statements)

References 50 publications

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“…Let K 0 be a diagonal matrix. Using (4)-( 5) the dynamics can be reformulated 2 as (12) with n = C(q, q) + g(q). Analogous to (7), the motor torque is composed of slow and fast terms as…”

Section: A Mpc-fast (Motor Dynamics)mentioning

confidence: 99%

“…By means of SP [32] and through the substitution in (12) the fast time scale can be introduced as ν = t/ with time 2 Dependencies on the states have been omitted for the sake of readability.…”

Section: A Mpc-fast (Motor Dynamics)mentioning

confidence: 99%

“…A technique that addresses these drawbacks is Model Predictive Control (MPC). It has been applied across different areas, such as autonomous driving [10], drones [11], or interconnected tanks [12]. Extensions of the standard linear or non-linear MPC involve passivitybased state constraints [13], the addition of safety filters [14], the consideration of process uncertainty [15], or various learning components [16].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Model Predictive Control for Flexible Joint Robots

Iskandar¹,

Ommeren²,

Wu³

et al. 2022

Preprint

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Modern Lightweight robots are constructed to be collaborative, which often results in a low structural stiffness compared to conventional rigid robots. Therefore, the controller must be able to handle the dynamic oscillatory effect mainly due to the intrinsic joint elasticity. Singular perturbation theory makes it possible to decompose the flexible joint dynamics into fast and slow subsystems. This model separation provides additional features to incorporate future knowledge of the jointlevel dynamical behavior within the controller design using the Model Predictive Control (MPC) technique. In this study, different architectures are considered that combine the method of Singular Perturbation and MPC. For Singular Perturbation, the parameters that influence the validity of using this technique to control a flexible-joint robot are investigated. Furthermore, limits on the input constraints for the future trajectory are considered with MPC. The position control performance and robustness against external forces of each architecture are validated experimentally for a flexible joint robot.

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“…Let K 0 be a diagonal matrix. Using (4)-( 5) the dynamics can be reformulated 2 as (12) with n = C(q, q) + g(q). Analogous to (7), the motor torque is composed of slow and fast terms as…”

Section: A Mpc-fast (Motor Dynamics)mentioning

confidence: 99%

Section: A Mpc-fast (Motor Dynamics)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model Predictive Control for Flexible Joint Robots

Iskandar¹,

Ommeren²,

Wu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…However, when some knowledge about the disturbances or uncertainty of the model is available, this approach does not take this into account. Stochastic MPC was developed to considering the possibility of some optimality or economic benefits, which can be gained by moving towards or violating those disturbances' boundaries [77,94]. Stochastic MPC can handle states and model parametric uncertainties and independent disturbances by employing the information about the mean and variance of the prediction states, parameters, and disturbances to make sure that possible violation of the constraints remain admissible relative to a predefined threshold [78].…”

Section: Dynamic Of the Systemmentioning

confidence: 99%

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

et al. 2021

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An internal combustion engine (ICE) is a highly nonlinear dynamic and complex engineering system whose operation is constrained by operational limits, including emissions, noise, peak in-cylinder pressure, combustion stability, and actuator constraints. To optimize today’s ICEs, seven to ten control actuators and 10–20 feedback sensors are often used, depending on the engine applications and target emission regulations. This requires extensive engine experimentation to calibrate the engine control module (ECM), which is both cumbersome and costly. Despite these efforts, optimal operation, particularly during engine transients and to meet real driving emission (RDE) targets for broad engine speed and load conditions, has still not been obtained. Methods of model predictive control (MPC) have shown promising results for real-time multi-objective optimal control of constrained multi-variable nonlinear systems, including ICEs. This paper reviews the application of MPC for ICEs and analyzes the recent developments in MPC that can be utilized in ECMs. ICE control and calibration can be enhanced by taking advantage of the recent developments in the field of Artificial Intelligence (AI) in applying Machine Learning (ML) to large-scale engine data. Recent developments in the field of ML-MPC are investigated, and promising methods for ICE control applications are identified in this paper.

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“…To tackle this issue, robust techniques such as robust MPC [108,109] and stochastic MPC [110,111] could be investigated for the optimal control of MicroCSP and building HVAC system. • Machine Learning-Based Control: In recent years, artificial intelligence (AI) has infiltrated all areas, and.…”

mentioning

confidence: 99%

Integration and Optimal Control of MicroCSP with Building HVAC Systems: Review and Future Directions

et al. 2021

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Heating, ventilation, and air-conditioning (HVAC) systems are omnipresent in modern buildings and are responsible for a considerable share of consumed energy and the electricity bill in buildings. On the other hand, solar energy is abundant and could be used to support the building HVAC system through cogeneration of electricity and heat. Micro-scale concentrated solar power (MicroCSP) is a propitious solution for such applications that can be integrated into the building HVAC system to optimally provide both electricity and heat, on-demand via application of optimal control techniques. The use of thermal energy storage (TES) in MicroCSP adds dispatching capabilities to the MicroCSP energy production that will assist in optimal energy management in buildings. This work presents a review of the existing contributions on the combination of MicroCSP and HVAC systems in buildings and how it compares to other thermal-assisted HVAC applications. Different topologies and architectures for the integration of MicroCSP and building HVAC systems are proposed, and the components of standard MicroCSP systems with their control-oriented models are explained. Furthermore, this paper details the different control strategies to optimally manage the energy flow, both electrical and thermal, from the solar field to the building HVAC system to minimize energy consumption and/or operational cost.

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A Comparative Study of Stochastic Model Predictive Controllers

Cited by 16 publications

References 50 publications

Model Predictive Control for Flexible Joint Robots

Model Predictive Control for Flexible Joint Robots

Model Predictive Control of Internal Combustion Engines: A Review and Future Directions

Integration and Optimal Control of MicroCSP with Building HVAC Systems: Review and Future Directions

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